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Environmental fate & pathways

Bioaccumulation: aquatic / sediment

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Reference
Endpoint:
bioaccumulation in aquatic species: fish
Type of information:
calculation (if not (Q)SAR)
Adequacy of study:
weight of evidence
Reliability:
4 (not assignable)
Rationale for reliability incl. deficiencies:
secondary literature
Justification for type of information:
Data is from authoritative database
Qualifier:
according to
Guideline:
other: Refer below principle
Principles of method if other than guideline:
Prediction done using OPERA (OPEn (quantitative) structure-activity Relationship Application) V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)
GLP compliance:
not specified
Specific details on test material used for the study:
- Name of test material (as cited in study report): 4-Methylpiperazin-1-amine
- Molecular formula: C5H13N3
- Molecular weight: 115.179 g/mol
- Substance type: organic
- Physical state: Liquid
- Smiles notation: N1(CCN(CC1)C)N
- InChl: 1S/C5H13N3/c1-7-2-4-8(6)5-3-7/h2-6H2,1H3
Radiolabelling:
not specified
Vehicle:
not specified
Test organisms (species):
other: Fish
Route of exposure:
aqueous
Test type:
other: predicted data
Water / sediment media type:
natural water: freshwater
Reference substance (positive control):
not specified
Type:
BCF
Value:
2.42 dimensionless
Basis:
other: Result based on the OECD principle 1-5
Calculation basis:
other: PaDEL descriptors
Remarks on result:
other: other details not available

Prediction based on following 5 OECD principles:

OECD Principle 1 (Defining the endpoint):

The original data collected from the PhysProp database (685 chemicals) has undergone a series of processes to curate the chemical structures and remove duplicates, obvious outliers and erroneous entries. This procedure also included a consistency check to ensure only good quality data is used for the development of the QSAR model (618 chemicals).

 

Then, QSAR-ready structures were generated by standardizing all chemical structures and removing duplicates, inorganic and metallo-organic chemicals (608 chemicals).

 

The curated outlier-free Physprop data inculding PBDEs additional data (626 chemicals) was divided into training and validation sets before the machine learning and modeling steps.

 

OECD Principle 2(Defining the algorithm):

Type of model:

QSAR model using PaDEL descriptors

 

Explicit algorithm:

Distance weighted k-nearest neighbors (kNN) This is a refinement of the classical k-NN classification algorithm where the contribution of each of 4.Defining the algorithm - OECD Principle 2 the k neighbors is weighted according to their distance to the query point, giving greater weight to

closer neighbors.The used distance is the Euclidean distance. kNN is an unambiguous algorithm that fulfills the transparency requirements of OECD principle 2 with an optimal compromise between model complexity and performance.

 

OECD Principle 3(Defining the applicability domain):

Method used to assess the applicability domain:The applicability domain of the model is assessed in two independent levels using two different distance-based methods. First, a global applicability domain is determined by means of the leverage approach that checks whether the query structure falls within the multidimensional chemical space of the whole training set. The leverage of a query chemical is proportional to its Mahalanob is distance measure from the centroid of the training set. The leverages of a given dataset are obtained from the diagonal values of the hat matrix. This approach is associated with a threshold leverage that corresponds 5.Defining the applicability domain - OECD Principle 3 to 3*p/n where p is the number of model variables while n is the number of training compounds. A query chemical with leverage higher than the threshold is considered outside the AD and can be associated with unreliable prediction. The leverage approach has specific limitations, in particular with respects to gaps within the descriptor space of the model or at the boundaries of the training set. To obviate such limitations, a second tier of applicability domain assessement was added. This comprised a local approach which only investigated the vicinity of the query chemical. This local approach provides a continuous index ranging from 0 to 1 which is different from the first approach which only provides Boolean answers (yes/no). This local AD-index is relative to the similarity of the query chemical to its 5 nearest neighbors in the p dimensional space of the model. The higher this index, the more the prediction is likely to be reliable.

 

OECD Principle 4 (Internal validation):

Availability of the training set: Yes

Statistics for goodness-of-fit: Performance in training: R2=0.85 RMSE=0.53

Robustness - Statistics obtained by leave-many-out cross-validation: Performance in 5-fold cross-validation: Q2=0.84 RMSE=0.55

 

OECD Principle 4(External validation):

Availability of the external validation set: Yes

.Predictivity - Statistics obtained by external validation: Performance in test: R2=0.83 RMSE=0.64

Experimental design of test set:

The structures are randomly selected to represent 25% of the available data keeping a similar normal distrubution of LogKoc vlaues in both training and test sets using the Venetian blinds method.

 

OECD Principle 5 (Providing a mechanistic interpretation):

Mechanistic basis of the model:

The model descriptors were selected statistically but they can also be mechanistically interpreted.Several publications reported a general bilinear

correlation pattern between BCF and logKow [Section 9.2 ref 3-6]. In our model, we used descriptors related to lipophilicity with different

methods and encoding different information (XLogP, CrippenLogP and ALogP) Since the number of H-bond acceptor atoms explains the tendency of polar chemicals towards aquatic partitioning two related descriptors were selected (nHBAcc, LipinskiFailures).The number of acidic groups (nAcid) is also a descriptor that encodes information about the partitioning of a chemical between the orgainc and water phases. Factors that increase intermolecular interactions (hydrogen bonding and polarity) lower the bioconcentration factor by making molecules remain in the aqueous phase, or cause binding to membranes and thereby hinder penetration into the organism.Dearden and Shinnawei demonstrated that the electronic properties

(polarizability and electronegativity) are of high significance to BCF modeling. Such information is encodd in these descriptors (minsC, naasC

and ATSC1s). van de Waals volume, here encoded in (ATSC0v) was shown to be of utility to BCF modeling by Papa et al.

Validity criteria fulfilled:
not specified
Conclusions:
The bioaccumulation factor i.e BCF for test substance 4-Methylpiperazin-1-amine was estimated to be 2.42 dimensionless.
Executive summary:

From CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application)  V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)  the bioaccumulation i.e BCF for test substance 4-Methylpiperazin-1-amine was estimated to be 2.42 dimensionless . The predicted BCF result based on the 5 OECD principles. Thus based on the result it is concluded that the test substance 4-Methylpiperazin-1-aminee is non-bioaccumulative in nature, because the bioconcentration factor in fish is less than 2000.

Description of key information

From BCFBAF Program (v3.00) model of EPI suite the estimated bio concentration factor (BCF) for 4-Methylpiperazin-1-amine (CAS No: 6928-85-4) is 3.162 L/kg wet-wt which does not exceed the bioconcentration threshold of 2000.Thus it is concluded that the test chemical 4-Methylpiperazin-1-amine is not expected to bio accumulate in the aquatic environment.

Key value for chemical safety assessment

BCF (aquatic species):
3.162 L/kg ww

Additional information

Various predicted data for the target compound 4-Methylpiperazin-1-amine (CAS No. 6928-85-4) and supporting weight of evidence study for its read across chemical were reviewed for the bioaccumulation end point which are summarized as below:

From BCFBAF Program (v3.00) model of EPI suite( Estimation Program Interface, 2017) the estimated bio concentration factor (BCF) for 4-Methylpiperazin-1-amine (CAS No: 6928-85-4) is 3.162 L/kg wet-wt which does not exceed the bioconcentration threshold of 2000.

Another prediction done by using Bio-concentration Factor (v12.1.0.50374)module of ACD (Advanced Chemistry Development)/I-Lab predictive module, 2017). Bio-concentration Factor ( BCF) at pH range 1-14 of the chemical 4-methylpiperazin-1-amine ( CAS no.6928 -85 -4) estimated to be 1 dimensionless.

Next predicted Bioconcentration factor (BCF) for test chemical 4-Methylpiperazin-1-amine (CAS No: 6928-85-4) in aquatic organisms by SciFinder database of American Chemical Society ( ACS, 2017) at pH 1-10 and temperature 25 °C .The Bioconcentration factor (BCF) of test substance 4-Methylpiperazin-1-amine at pH 1-10 and temperature 25 °C was estimated to be 1 dimensionless.

The Bioconcentration factor (BCF) for test chemical 4-Methylpiperazin-1-amine (CAS No: 6928-85-4) was predicted in aquatic organisms by Chemspider- ACD/PhysChem Suite prediction model ( 2017) at pH 5.5 and pH 7.4.The Bioconcentration factor (BCF) of test substance 4-Methylpiperazin-1-amine at pH 5.5 and pH 7.4 was estimated to be 1 dimensionless. This BCF value suggests that the test chemical 4-Methylpiperazin-1-amine is non bioaccumulative in aquatic organisms.

From CompTox Chemistry Dashboard using OPERA (OPEn (quantitative) structure-activity Relationship Application)  V1.02 model in which calculation based on PaDEL descriptors (calculate molecular descriptors and fingerprints of chemical)  the bioaccumulation i.e. BCF for test substance 4-Methylpiperazin-1-amine was estimated to be 2.42 dimensionless . The predicted BCF value is based on the 5 OECD principles.

In a supporting weight of evidence study for Environmental Bioconcentration factor of read across chemical Piperazine (CAS No: 110-85-0) was performed in orange-red killifish (Oryzias latipes). In this test, Orange-red killifish (Oryzias latipes) were exposed to 1 and 0.1 ppm of read across chemical over an 8-week period. BCF values less than 0.3 to 0.9 and less than 3.9 were measured at concentrations 1 and 0.1 ppm, respectively. According to a classification scheme, these BCF values suggest the potential for bioconcentration of read across chemical Piperazine (CAS No: 110-85-0) in aquatic organisms is low.

In another weight of evidence study for read across chemical 1, 4-diaminobenzene (CAS No: 106-50-3) was obtained from authoritative HSDB database. An estimated BCF of 0.3 dimensionless was calculated for 1, 4-diaminobenzene, using a log Kow of -0.3 and a regression-derived equation. According to a classification scheme, this BCF suggests the potential for bioconcentration of read cross chemical 1, 4-diaminobenzene in aquatic organisms is low.

On the basis of above results for target chemical 4-Methylpiperazin-1-amine (from EPI suite, ACD labs, Sci Finder database, Chemspider and CompTox Chemistry Dashboard 2017) and for its read across chemicals (From HSDB) it can be concluded that the BCF value of test substance 4-Methylpiperazin-1-amine ranges from 1-3.162 dimensionless which does not exceed the bioconcentration threshold of 2000, indicating that the chemical 4-Methylpiperazin-1-amine is expected to be non-bioaccumulative in the food chain.